{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "60cd5bb4",
   "metadata": {},
   "source": [
    "# Similarity explanations for MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11fe0610",
   "metadata": {},
   "source": [
    "In this notebook, we apply the similarity explanation method to a convolutional network trained on the MNIST dataset. \n",
    "Given an input image of interest, the similarity explanation method used here aims to find images in the training dataset that are similar to the image of interest according to \"how the model sees them\", meaning that the similarity metric makes use of the gradients of the model's loss function with respect to the model's parameters. \n",
    "The explanation should be interpreted along the line of \"I classify this image as a 4 because I find it similar to another image in the training set that was labeled as a 4.\"\n",
    "\n",
    "The similarity explanation tool supports both `pytorch` and `tensorflow` backends. In this example, we will use the tensorflow backend.\n",
    "\n",
    "A more detailed description of the method can be found [here](../methods/Similarity.ipynb). The implementation follows  [Charpiat et al., 2019](https://papers.nips.cc/paper/2019/hash/c61f571dbd2fb949d3fe5ae1608dd48b-Abstract.html) and  [Hanawa et al. 2021](https://arxiv.org/abs/2006.04528)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53a109a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Activation, Conv2D, Dense, Dropout\n",
    "from tensorflow.keras.layers import Flatten, Input, Reshape, MaxPooling2D\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.losses import categorical_crossentropy\n",
    "from alibi.explainers import GradientSimilarity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72415e77",
   "metadata": {},
   "source": [
    "## Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b486512",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_similar(ds, expls, figsize=(20, 20)):\n",
    "    \"\"\"Plots original instances and similar instances.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    ds \n",
    "        List of dictionaries containing instances to plot, labels and predictions.\n",
    "    expls\n",
    "        Similarity explainer explanation object.\n",
    "    figsize\n",
    "        Figure size.\n",
    "    \n",
    "    Returns\n",
    "    ------\n",
    "    None\n",
    "    \"\"\"\n",
    "    fig, axes = plt.subplots(5, 6, figsize=figsize, sharex=False)\n",
    "    for j in range(len(ds)):\n",
    "        d = ds[j]\n",
    "        axes[j, 0].imshow(np.squeeze(d['x']), cmap='gray')\n",
    "        axes[j, 0].axis('off')\n",
    "        if j == 0:\n",
    "            title_orig = \"Original instance\"\n",
    "            axes[j, 0].set_title(f\"{title_orig} \\n\" + \n",
    "                                 f\"{len(title_orig) * '='} \\n\" + \n",
    "                                 f\"Label: {d['y']} - Prediction: {d['pred']} \")\n",
    "        else:\n",
    "            axes[j, 0].set_title(f\"Label: {d['y']} - Prediction: {d['pred']} \")\n",
    "        for i in range(expls.data['most_similar'].shape[0]):\n",
    "            most_similar = np.squeeze(expls.data['most_similar'][j][i])\n",
    "            axes[j, i + 1].imshow(most_similar, cmap='gray')\n",
    "            axes[i, i + 1].axis('off')\n",
    "            if j == 0:\n",
    "                title_most_sim = f\"{i+1}{append_int(i+1)} most similar instance\"\n",
    "                axes[j, i + 1].set_title(f\"{title_most_sim} \\n\" + \n",
    "                                         f\"{len(title_most_sim) * '='} \\n\"+ \n",
    "                                         f\"Label: {d['y_sim'][i]} - Prediction: {d['preds_sim'][i]}\")\n",
    "            else:\n",
    "                axes[j, i + 1].set_title(f\"Label: {d['y_sim'][i]} - Prediction: {d['preds_sim'][i]}\")\n",
    "        \n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def plot_distributions(ds, expls, figsize=(20, 20)):\n",
    "    \"\"\"Plots original instances and scores distributions per class.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    ds \n",
    "        List of dictionaries containing instances to plot, labels and predictions.\n",
    "    expls\n",
    "        Similarity explainer explanation object.\n",
    "    figsize\n",
    "        Figure size.\n",
    "    \n",
    "    Returns\n",
    "    ------\n",
    "    None\n",
    "    \"\"\"\n",
    "\n",
    "    fig, axes = plt.subplots(5, 3, figsize=figsize, sharex=False)\n",
    "\n",
    "    for i in range(len(ds)):\n",
    "        d = ds[i]\n",
    "\n",
    "        y_sim = d['y_sim']\n",
    "        preds_sim = d['preds_sim']\n",
    "        y = d['y']\n",
    "        pred = d['pred']\n",
    "        df_ditribution = pd.DataFrame({'y_sim': y_sim, \n",
    "                                       'preds_sim': preds_sim, \n",
    "                                       'scores': expls.data['scores'][i]})\n",
    "\n",
    "        axes[i, 0].imshow(np.squeeze(d['x']), cmap='gray')\n",
    "        axes[i, 0].axis('off')\n",
    "        if i == 0:\n",
    "            title_orig = \"Original instance\"\n",
    "            axes[i, 0].set_title(f\"{title_orig} \\n \" + \n",
    "                                 f\"{len(title_orig) * '='} \\n\" + \n",
    "                                 f\"Label: {d['y']} - Prediction: {d['pred']} \")\n",
    "        else:\n",
    "            axes[i, 0].set_title(f\"Label: {d['y']} - Prediction: {d['pred']}\")        \n",
    "        df_y = df_ditribution.groupby('y_sim')['scores'].mean().sort_values(ascending=False)\n",
    "        df_y.plot(kind='bar', ax=axes[i, 1])\n",
    "        if i == 0:\n",
    "            title_true_class = \"Averaged scores for each true class in reference set\"\n",
    "            axes[i, 1].set_title(f\"{title_true_class} \\n\" + \n",
    "                                 f\"{len(title_true_class) * '='} \\n \")\n",
    "        df_preds = df_ditribution.groupby('preds_sim')['scores'].mean().sort_values(ascending=False)\n",
    "        df_preds.plot(kind='bar', ax=axes[i, 2])\n",
    "        if i == 0:\n",
    "            title_pred_class = \"Averaged scores for each predicted class in reference set\"\n",
    "            axes[i, 2].set_title(f\"{title_pred_class} \\n\" + \n",
    "                                 f\"{len(title_pred_class) * '='} \\n \")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "def append_int(num):\n",
    "    \"\"\"Converts an integer into an ordinal (ex. 1 -> 1st, 2 -> 2nd, etc.).\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    num\n",
    "        Integer number\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Ordinal suffixes\n",
    "    \"\"\"\n",
    "    if num > 9:\n",
    "        secondToLastDigit = str(num)[-2]\n",
    "        if secondToLastDigit == '1':\n",
    "            return 'th'\n",
    "    lastDigit = num % 10\n",
    "    if (lastDigit == 1):\n",
    "        return 'st'\n",
    "    elif (lastDigit == 2):\n",
    "        return 'nd'\n",
    "    elif (lastDigit == 3):\n",
    "        return 'rd'\n",
    "    else:\n",
    "        return 'th'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deef2d9c",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30eb0dd5",
   "metadata": {},
   "source": [
    "Loading and preparing the MNIST data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9ce7c3fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 1) (60000, 10) (10000, 28, 28, 1) (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "train, test = tf.keras.datasets.mnist.load_data()\n",
    "X_train, y_train = train\n",
    "X_test, y_test = test\n",
    "test_labels = y_test.copy()\n",
    "train_labels = y_train.copy()\n",
    "                         \n",
    "X_train = X_train.reshape(-1, 28, 28, 1).astype('float64') / 255\n",
    "X_test = X_test.reshape(-1, 28, 28, 1).astype('float64') / 255\n",
    "y_train = to_categorical(y_train, 10)\n",
    "y_test = to_categorical(y_test, 10)\n",
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6215e82",
   "metadata": {},
   "source": [
    "## Train model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceb8a527",
   "metadata": {},
   "source": [
    "Train a convolutional neural network on the MNIST dataset. The model includes 2 convolutional layers and it reaches a test accuracy of 0.98. If `save_model = True`, a local folder `./model_mnist` will be created and the trained model will be saved in that folder. If the model was previously saved, it can be loaded by setting `load_mnist_model = True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3fc11566",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_mnist_model = False\n",
    "save_model = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2a69e5b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "filepath = './model_mnist/'  # change to directory where model is saved\n",
    "if load_mnist_model:\n",
    "    model = tf.keras.models.load_model(os.path.join(filepath, 'model.h5'))\n",
    "else:\n",
    "    # define model\n",
    "    inputs = Input(shape=(X_train.shape[1:]), dtype=tf.float64)\n",
    "    x = Conv2D(64, 2, padding='same', activation='relu')(inputs)\n",
    "    x = MaxPooling2D(pool_size=2)(x)\n",
    "    x = Dropout(.3)(x)\n",
    "    \n",
    "    x = Conv2D(32, 2, padding='same', activation='relu')(x)\n",
    "    x = MaxPooling2D(pool_size=2)(x)\n",
    "    x = Dropout(.3)(x)\n",
    "    \n",
    "    x = Flatten()(x)\n",
    "    x = Dense(256, activation='relu')(x)\n",
    "    x = Dropout(.5)(x)\n",
    "    logits = Dense(10, name='logits')(x)\n",
    "    outputs = Activation('softmax', name='softmax')(logits)\n",
    "    model = Model(inputs=inputs, outputs=outputs)\n",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    \n",
    "    # train model\n",
    "    model.fit(X_train,\n",
    "              y_train,\n",
    "              epochs=6,\n",
    "              batch_size=256,\n",
    "              verbose=1,\n",
    "              validation_data=(X_test, y_test)\n",
    "              )\n",
    "    if save_model:\n",
    "        if not os.path.exists(filepath):\n",
    "            os.makedirs(filepath)\n",
    "        model.save(os.path.join(filepath, 'model.h5'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02562289",
   "metadata": {},
   "source": [
    "## Find similar instances"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15347ecd",
   "metadata": {},
   "source": [
    "Initializing a `GradientSimilarity` explainer instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae358708",
   "metadata": {},
   "outputs": [],
   "source": [
    "gsm = GradientSimilarity(model, categorical_crossentropy, precompute_grads=True, sim_fn='grad_cos')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eefdcda8",
   "metadata": {},
   "source": [
    "Selecting a reference set of 1000 random samples from the training set. The `GradientSimilarity` explainer will find the most similar instances among those. This downsampling step is performed  to speed up the `fit` step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "84d9e52d",
   "metadata": {},
   "outputs": [],
   "source": [
    "idxs_ref = np.random.choice(len(X_train), 1000, replace=False)\n",
    "X_ref, y_ref = X_train[idxs_ref], y_train[idxs_ref]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ce28e86",
   "metadata": {},
   "source": [
    "Fitting the explainer on the reference data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a0ca836c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientSimilarity(meta={\n",
       "  'name': 'GradientSimilarity',\n",
       "  'type': ['whitebox'],\n",
       "  'explanations': ['local'],\n",
       "  'params': {\n",
       "              'sim_fn_name': 'grad_cos',\n",
       "              'store_grads': True,\n",
       "              'backend_name': 'tensorflow',\n",
       "              'task_name': 'classification'}\n",
       "            ,\n",
       "  'version': '0.6.6dev'}\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsm.fit(X_ref, y_ref)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2877c0b",
   "metadata": {},
   "source": [
    "Selecting 5 random instances from the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "91d8d807",
   "metadata": {},
   "outputs": [],
   "source": [
    "idxs_samples = np.random.choice(len(X_test), 5, replace=False)\n",
    "X_sample, y_sample = X_test[idxs_samples], y_test[idxs_samples]\n",
    "preds = model(X_sample).numpy().argmax(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "539b06f2",
   "metadata": {},
   "source": [
    "Getting the most similar instances for the each of the 5 test samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9ce5cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "expls = gsm.explain(X_sample, y_sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc6df5fa",
   "metadata": {},
   "source": [
    "## Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f7190aa",
   "metadata": {},
   "source": [
    "Building a dictionary for each sample for visualization purposes. \n",
    "Each dictionary contains\n",
    "\n",
    "* The original image `x`.\n",
    "* The corresponding label `y`.\n",
    "* The corresponding model's prediction `pred`.\n",
    "* The corresponding reference labels ordered by similarity `y_sim`.\n",
    "* The corresponding model's predictions for the reference set `preds_sim`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7871cead",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = []\n",
    "for j in range(len(X_sample)):\n",
    "    y_sim = y_ref[expls.data['ordered_indices'][j]].argmax(axis=1)\n",
    "    X_sim = X_ref[expls.data['ordered_indices'][j]]\n",
    "    preds_sim = model(X_sim).numpy().argmax(axis=1)\n",
    "\n",
    "    d = {'x': X_sample[j], \n",
    "         'y': y_sample[j].argmax(), \n",
    "         'pred':preds[j],\n",
    "         'y_sim': y_sim, \n",
    "         'preds_sim': preds_sim}\n",
    "    ds.append(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08fe9792",
   "metadata": {},
   "source": [
    "Showing the 5 most similar instances for each of the test instances, ordered from the most similar to the least similar."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b03ca2b",
   "metadata": {},
   "source": [
    "### Most similar instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "88496684",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_similar(ds, expls)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c48229b9",
   "metadata": {},
   "source": [
    "### Most similar labels distributions "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f87af64",
   "metadata": {},
   "source": [
    "Showing the average similarity scores for each group of instances in the reference set belonging to the same true class and to same predicted class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c7384c2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distributions(ds, expls)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "379a01ed",
   "metadata": {},
   "source": [
    "The plots show how the instances belonging to the same class (and the instances classified by the model as belonging to the same class) of the instance of interest  have on average higher similarity scores, as expected. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
